Table 3 Results from the pre-curriculum survey consisting of eight questions
Pre-curriculum survey (n = 17 residents) | ||
|---|---|---|
In which hospital did you originally enroll for the radiology residency program? | Academic hospital | 12 (70.6%) |
Nonacademic hospital | 5 (29.4%) | |
What is the extent of your knowledge and experience in the field of AI? | No experience with AI | 1 (5.9%) |
Heard about AI | 11 (64.7%) | |
Had some lectures about AI | 8 (47.1%) | |
Engaged with AI | 6 (35.3%) | |
How important do you consider education in AI in the radiology residency program? | Crucial | 2 (17.6%) |
Necessary | 8 (47.1%) | |
Important | 4 (23.5%) | |
Interesting | 1 (5.9%) | |
Fine | 1 (5.9%) | |
Not necessary | 0 (0%) | |
Which specific topics would you prioritize for inclusion in the course (multiple options possible)? | How to implement AI in the workflow of the radiologist | 15 (88.2%) |
Understanding about machine learning and deep learning | 7 (41.2%) | |
How can AI be used in clinical practice | 12 (70.6%) | |
How can AI be used for research purposes | 10 (58.8%) | |
Which learning method would you suggest for the course (multiple options possible)? | Integration education in AI into the clinical rotations | 10 (58.8%) |
Separate learning course | 9 (52.9%) | |
Online learning module | 8 (47.1%) | |
What duration do you recommend for the course? | Continuous time to the radiology residency program | 1 (5.9%) |
longer than 1 month | 4 (23.5%) | |
1 month | 4 (23.5%) | |
3 weeks | 1 (5.9%) | |
1 week | 6 (35.3%) | |
How much time are you willing to devote to self-study and coursework outside of regular working hours? | Only during regular working hours | 3 (17.6%) |
1 day | 3 (17.6%) | |
1 week | 6 (35.3%) | |
1 month | 4 (23.5%) | |
As long as needed | 1 (5.9%) | |
Do you have any suggestions or recommendations for the course? | Comment: I feel that there is too much repetition in discussions about neural networks and how they work. I am particularly interested in information regarding the clinical and research applications of AI. Experts in this field can provide guidance and fully comprehend the details of neural networks. I think it would be a stretch for all radiologists/residents to become experts in this area, but basic knowledge seems appropriate Do’s: Teach specific terminology so that residents can independently read and critique AI-related articles. Provide an overview of data augmentation techniques to enhance the robustness of the algorithm, such as duplicating, rotating, and flipping the training set Don’ts: Analyze the CNN architecture in detail. In my opinion, this adds little value to the concept, and most physicians will not absorb enough information to retain it Comment: It is especially important that we understand these concepts for the future. As AI becomes more prevalent, we will need to know more about it to assume a more supervisory role. Therefore, what will our role be in this? | |